Open AccessShort-term wind power forecasting model based on multi-feature extraction and CNN-LSTM
Author(s) -
Honghai Kuang,
Qian Guo,
Shengqing Li,
Hao Zhong
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/702/1/012019
Subject(s) - computer science , term (time) , autoregressive integrated moving average , artificial intelligence , cluster analysis , feature extraction , deep learning , feature (linguistics) , wind power , wind power forecasting , pattern recognition (psychology) , power (physics) , machine learning , time series , electric power system , engineering , linguistics , philosophy , physics , quantum mechanics , electrical engineering
Improving the accuracy of short-term wind power forecasting is critical to wind power consumption. This paper establishes a short-term wind power prediction model based on the multi-feature extraction and deep learning network CNN-LSTM. Muti-features are extracted from original data to improve the accuracy of training. In addition, clustering algorithm is used to classify training data and train the models corresponding to those classes. CNN-LSTM prediction models are established for each cluster and compared with ARIMA, RNN, CNN and LSTM models.